Given that our extractor performs a nondifferentiable hard extraction, we apply standard policy gradient methods to bridge the backpropagation and form an end-to-end trainable (stochastic) computation graph.

simply starting from a randomly initialized network to train the whole model in an end-to-end fashion is infeasible.


(2017) added controlling parameters to adapt the summary to length, style, and entity preferences.

none of these used RL to bridge the non-differentiability of neural models.


Regression has primarily been attempted through support vector regression (Kim et al., 2006;Zhang and Varadarajan, 2006;Yang et al., 2015).

probabilistic matrix factorization (Tang et al., 2013), linear regression (Lu et al., 2010), and extended tensor factorization models (Moghaddam et al., 2012) have successfully been used to integrate sophisticated constraints into the learning process and have achieved improvements over regular regression models.


Back in 2012, in the case of "Nagoya", many native English speakers posted their pleasant travel experiences in Nagoya on Twitter.

chinese people overwhelmingly greeted the city with anger and condemnation on Weibo (a chinese version of Twitter), because the city mayor denied the truthfulness of the Nanjing Massacre.


Due to the dynamic nature of language and trending issues on Twitter, it is impracticable to construct a list of all possible phrases one can expect to appear in tweets.

because politicians are known for sticking to certain talking points, these phrases can be abstracted into higher-level phrases that are more stable and thus easier to identify and extract.


The difference in performance between the GOLD and MFD results shows that directly mapping the expected MFD unigrams to politicians' tweets is not informative enough for party affiliation prediction.

by using abstract representations of language, the PSL model is able to achieve results closer to that which can be attained when using the actual annotations as features.


The ablation results indicate that model uncertainty plays the most important role among the confidence metrics.

removing the metrics of data uncertainty affects performance less, because most examples in the datasets are in-domain.


Recent work has managed to learn crosslingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training.

their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in more realistic scenarios.


(2017) showed that an iterative self-learning method is able to bootstrap a high quality mapping from very small seed dictionaries (as little as 25 pairs of words).

their analysis reveals that the self-learning method gets stuck in poor local optima when the initial solution is not good enough, thus failing for smaller training dictionaries.


(2017b) adopt the earth mover's distance for training, optimized through a Wasserstein generative adversarial network followed by an alternating optimization procedure.

all this previous work used comparable Wikipedia corpora in most experiments and, as shown in Section 5, face difficulties in more challenging settings.


Previous studies (Collobert and Weston, 2008;Dong et al., 2015;Luong et al., 2016;Liu et al., 2018;Yang et al., 2017) have proven that MTL is an effective approach to boost the performance of related tasks such as MT and parsing.

most of these previous efforts focused on tasks and languages which have sufficient labeled data but hit a performance ceiling on each task alone.


In (Yang et al., 2017), the authors simulated a low-resource setting for English and Spanish by downsampling the training data for the target task.

for most low-resource languages, the data sparsity problem also lies in related tasks and languages.


The bidirectional LSTM layer is essential to extract character, word, and contextual information from a sentence.

with a large number of parameters, it cannot be fully trained only using the low-resource task data.


As Figure 7 shows, the performance goes up when we raise the sample rate from 1% to 20%.

we do not observe significant improvement when we further increase the sample rate.


By using BWEs based on only Subtitles, we lose too many embeddings of similar English and Spanish tweets.

if we use only tweet-based BWEs we lose good quality semantic knowledge which can be learned from more standard text domains.


Cloze-style reading comprehension is a task setting where the question is formed by replacing a token in a sentence of the story with a placeholder (left part of Figure 1).

to many previous complex models Dhingra et al., 2017;Cui et al., 2017;Munkhdalai and Yu, 2016; that perform multi-turn reading of a story and a question before inferring the correct answer, we aim to tackle the cloze-style RC task in a way that resembles how humans solve it: using, in addition, background knowledge.


(2017), by contrast, explicitly reads the acquired additional knowledge sequentially after reading the document and question, but transfers the background knowledge implicitly, by refining the word embeddings of the words in the document and the question with the words from the supporting knowledge that share the same lemma.

to the implicit knowledge transfer of Weissenborn et al.


In summary, previous work on text adversaries change semantics, only generate local (instancespecific) adversaries (Zhao et al., 2018;Iyyer et al., 2018), or are tailored for white-box models (Ebrahimi et al., 2018) or specific tasks (Jia and Liang, 2017).

sEAs expose oversensitivity for specific predictions of black-box models for a variety of tasks, while sEARs are intuitive and actionable global rules that induce a high number of high-quality adversaries.


We reproduced the results reported in (Hu et al., 2017) using their tasks and data.

the same model trained on our political slant datasets (described in §3), obtained an almost random accuracy of 50.98% in style transfer.


Unlike DBpedia and Freebase, Wikidata usually contains a very concise description for many of its entities.

because Wikidata is based on user contributions, many new entries are created that still lack such descriptions.


In this regard, such a model thus resembles our approach.

there are important differences between this baseline and our model.


However, the major shortcoming of these sorts of methods, including those aiming at more fine-grained typing, is that they assume that the set of candidate types is given as input, and the main remaining challenge is to pick the correct one(s).

our work yields descriptions that often indicate the type of entity, but typically are more natural-sounding and descriptive (e.g.


(2016) take Wikipedia infobox data as input and train a custom form of neural language model that, conditioned on occurrences of words in the input table, generates biographical sentences as output.

their system is limited to a single kind of description (biographical sentences) that tend to share a common structure.


They investigated an extensive array of current state-of-the-art neural pointer methods but found that template-based models outperform all neural models on this task by a significant margin.

their method requires specific templates for each domain (for example, basketball games in their case).


They find this technique can generate text of the desired genre, but the movie plots are not interpretable (as the model outputs events, not raw text).

we are not aware of previous work that has used hierarchical generation from a textual premise to improve the coherence and structure of stories.


When analyzing 500 150-word generated stories from test-set prompts, the average longest common subsequence is 8.9.

the baseline Conv seq2seq model copies 10.2 words on average and the KNN baseline copies all 150 words from a story in the training set.


The second seq2seq model learns to focus on rare words, such as horned and robe.


Using automatic metrics can ensure rapid prototyping and testing new models with fewer expensive human evaluation.

they have been criticized to be biased and correlate poorly with human judgments, especially in many generative tasks like response generation (Lowe et al., 2017;Liu et al., 2016), dialogue system (Bruni and Fernández, 2017) and machine translation (Callison-Burch et al., 2006).


In order to approximate the Reward Boltzmann distribution towards the "real" data distribution p * (W ), we design a min-max two-player game, where the Reward Boltzmann distribution p θ aims at maximizing the its similarity with empirical distribution p e while minimizing that with the "faked" data generated from policy model π β .

the policy distribution π β tries to maximize its similarity with the Boltzmann distribution p θ .


However, the references to the same image sequence are photostream different from each other, so the score is very low and not suitable for this task.

our AREL framework can lean a more robust reward function from human-annotated stories, which is able to provide better guidance to the policy and thus improves its performances over different metrics.


Glove+Picturebook improves over the Glove baseline for image search but falls short on image annotation.

using contextual gating results in improvements over the baseline on all metrics except R@1 for image annotation.


Previous works (She et al., 2014;Misra et al., 2015;Chai, 2016, 2017) explicitly model verb semantics as desired goal states and thus linking natural language commands with underlying planning systems for action planning and execution.

these studies were carried out either in a simulated world or in a carefully curated simple environment within the limitation of the robot's manipulation system.


Indeed, we can also observe that another CNN-based baseline, i.e., CNN-ASP implemented by us, also obtains good results on TWITTER.

the performance of those comparison methods is mostly unstable.


This also led to existing and potential research in improving attention modeling (discussed in Section 5).

we observed that simply focusing on tackling the target-context detection problem and learning better attention are not sufficient to solve the problem found in sentences (2), (3) and (4).


First, an aspect word often expresses no sentiment, for example, "screen".

if the aspect term v t is simply removed from Eq.


This technique is also intuitive in neural networks.

notice that by using the non-linear projection (or adding more sophisticated hidden layers) over them in this way, we sacrifice some interpretability.


For some simple examples like "the battery is good", the context word "good" simply indicates clear sentiment, which can be captured by their first-order term.

notice that the modeling of second-order terms offers additional help in both general and target-sensitive scenarios.


In fact, solely improving attention does not solve our problem (see Sections 1 and 3).

better attention can certainly help achieve an overall better performance for the ASC task, as it makes the targeted-context detection more accurate.


The cause of this unavoidable error is that W c i is not conditioned on the target.

w J d i , •d t tanh(w 2 c i ) can change the sentiment polarity with the aspect vector d t encoded.


Most of the works have focused on learning a shared low dimensional representation of features that can be generalized across different domains.

none of the approaches explicitly analyses significance and polarity of words across domains.


In other words, addition of the iterative process with the shared representation given by SCL overcomes the errors introduced by SCL.

sCP given by our approach were able to produce a less erroneous system in oneshot.


Table 5 shows that K→E outperforms B→E and D→E, and E→K outperforms B→K and D→K.

dVd (d) and electronics are two very different domains unlike electronics and Kitchen, or dVd and books.


We argue that content preservation is also an indispensable evaluation metric.

when applied to the sentiment-to-sentiment translation task, the previously mentioned models share the same problem.


is computed based on the percentage of overlapping n-grams between the generated text and the reference text.

the overlapping n-grams contain not only content words but also function words, bringing the noisy results.


proposed to use discourse markers to help rep-resent the meanings of the sentences.

they represent each sentence by a single vector and directly concatenate them to predict the answer, which is too simple and not ideal for the largescale datasets.


We observe that the values are highly correlated among the synonyms like "people" with "man", "three" with "3" in both situations.

words that might have contradictory meanings like "hoods" with "bareheaded", "quiet" with "busy" perform worse without the discourse markers augmentation, which conforms to the conclusion that the "contradiction" label examples benefit a lot which is observed in the Section 5.5.


They also apply their pre-trained sentence encoder to a series of natural language understanding tasks such as sentiment analysis, question-type, entailment, and relatedness.

all those datasets are provided by Conneau et al.


However, they manually group the discourse markers into several categories based on human knowledge and predict the category instead of the explicit discourse marker phrase.

the size of their dataset is much smaller than that in , and sometimes there has been disagreement among annotators about what exactly is the correct categorization of discourse relations (Hobbs, 1990).


While this allows some multi-step inferential process, these networks lack a more complex reasoning mechanism, needed for more elaborated tasks such as inferring relations among entities (relational reasoning).

relation Networks (rNs), proposed in Santoro et al.


A possibility is that each attention head is being adapted for different groups of related tasks.


The model shares some similarities with the Memory Network model.

unlike the MemNN model, it operates in the input sequentially (as in the NTM model).


That is because the episodic buffer has an integration function that our model does not cover.

that can be an interesting source of inspiration for next versions of the model that integrate both visual and textual information for question answering.


Then, we can proceed using the same architecture for the reasoning and attention module that the one used in the textual QA model.

for the visual QA task, we used an additive attention mechanism.


On short text, we also found that attention (or the gated pooling mechanism from GRNN) did not really help make any significant improvements over the vanilla LSTM model and a qualitative explanation to why this is so is deferred to the next section.

attention helps for long text (such as debates), resulting in Attention LSTMs becoming the strongest baseline on the Debates datasets.


This is in concert with our intuition about modeling contrast and incongruity.

both ATT-LSTM and ATT-RAW learn very different attention maps.


This time, the attention maps of MIARN are not as distinct as before.

they focus on sentiment-bearing words, composing the words 'ignored sucks' to form the majority of the intraattentive representation.


This behavior of gracefully falling back to NCE is more desirable than the alternative of stalled training if p − (y|x) does not have a simple p nce mixture component.

we would still like to avoid such collapse, as the adversarial samples provide greater learning signals than NCE samples.


As can be seen from our results in Table 2, ACE on RW is not always better and for the 100d and 300d Glove embeddings is marginally worse.

on WordSim353 ACE does considerably better across the board to the point where 50d Glove embeddings outperform the 300d baseline Glove model.


Divergence between F-Measure and Cross-Entropy Loss.

detection tasks are mostly evaluated using F-measure computed on positive classes, which makes it unsuitable to optimize classifiers using cross-entropy loss.


We can easily see that for accuracy metric, correct predictions of positive and negative instances are equally regarded (i.e., T P and T N are symmetric), which is consistent with crossentropy loss function.

when measuring using F-measure, this condition is no longer holding.


2) Static scaling sets the importance of negative instances statically in the entire training procedure.

as shown in Section 3, the rel- ative importance between different classes is dynamically changed during the training procedure, which makes static scaling incapable of achieving stable performance in different phases of training.


More formally, let us illustrate the model by taking the sequence prediction task (Figure 1) as illustration.

given an utterance with labels y 1 , .., y n , our Multi-task Tri-training loss consists of three task-specific (m 1 , m 2 , m 3 ) tagging loss functions (where h is the uppermost Bi-LSTM encoding): to classic tri-training, we can train the multi-task model with its three model-specific outputs jointly and without bootstrap sampling on the labeled source domain data until convergence, as the orthogonality constraint enforces different representations between models m 1 and m 2 .


Note that we constrain the size of S t not to exceed |S * | (the 7th line in Algorithm 1) to avoid that too many fluency boost pairs overwhelm the effects of the original error-corrected pairs on model learning.

to back-boost learning whose core idea is originally from NMT, self-boost learning is original, which is specially devised for neural GEC.


Take our best system (the last row in Table  2) as an example, among 1,312 sentences in the CoNLL-2014 dataset, seq2seq inference with shallow fusion LM edits 566 sentences.

fluency boost inference additionally edits 23 sentences during the second round inference, improving F 0.5 from 52.59 to 52.72.


Most of advanced GEC systems are classifierbased (Chodorow et al., 2007;De Felice and Pulman, 2008;Han et al., 2010;Leacock et al., 2010;Tetreault et al., 2010a;Dale and Kilgarriff, 2011) 7 The state-of-the-art result on CoNLL-2014 dataset has been recently advanced by Chollampatt and Ng (2018) (F0.5=54.79) and Grundkiewicz and Junczys-Dowmunt (2018) (F0.5=56.25), which are contemporaneous to this paper.

to the basic seq2seq model in this paper, they used advanced approaches for modeling (e.g., convolutional seq2seq with pre-trained word embedding, using edit operation features, ensemble decoding and advanced model combinations).


The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data.

obtaining labeled data is a big challenge in many real-world problems.


We initialize the embedding matrix E in our network with word embeddings that are pretrained on a large crisis dataset (Subsection 2.5).

embedding matrix E can also be initialize randomly.


Typically graphs are constructed based on a relational knowledge source, e.g., citation links in (Lu and Getoor, 2003), or distance between instances (Zhu, 2005).

we do not have access to such a relational knowledge in our setting.


On one hand, this is actually determined by real distribution of ratings for a particular prompt, e.g., how many essays are with an extreme quality for a given prompt in the target data.

a fine-grained tuning of the RankSVM (e.g., tuning C + and C − for positive and negative exam-ples separately) may partially resolve the problem, which is left for the future work.


In machine translation, since every sentence and its translation are semantically equivalent, there exists a 1-to-1 relationship between them.

in general purpose dialog, a general response (e.g., "I don't know") could correspond to a large variety of input utterances.


(2017) incorporated the topic information from an external corpus into the Seq2Seq framework to guide the generation.

external dataset may not be always available or consistent with the conversation dataset in topics.


In the usage space, words like "脂 肪 肝(fatty liver)" and "久 坐(outsit)" lie closely which are both specific words, and both are far from the general words like "胖(fat)".

in the semantic space, "脂 肪 肝(fatty liver)" is close to "胖(fat)" since they are semantically related, and both are far from the word "久坐(outsit)".


Previous studies show that capturing those matched segment pairs at different granularities across context and response is the key to multiturn response selection (Wu et al., 2017).

existing models only consider the textual relevance, which suffers from matching response that latently depends on previous turns.


As demonstrated, important matching pairs in selfattention-match in stack 0 are nouns, verbs, like "package" and "packages", those are similar in topics.

matching scores between prepositions or pronouns pairs, such as "do" and "what", become more important in self-attention-match in stack 4.


Generating emotional language is a key step towards building empathetic natural language processing agents.

a major challenge for this line of research is the lack of large-scale labeled training data, and previous studies are limited to only small sets of human annotated sentiment labels.


In recent years, a handful of medium to large scale, emotional corpora in the area of emotion analysis (Go et al., 2016) and dialog (Li et al., 2017b) are proposed.

all of them are limited to a traditional, small set of labels, for example, "happiness," "sadness," "anger," etc.


VAE (Kingma and Welling, 2013) encodes data in a probability distribution, and then samples from the distribution to generate examples.

the original frameworks do not support end-to-end generation.


Social media is a natural source of conversations, and people use emojis extensively within their posts.

not all emojis are used to express emotion and frequency of emojis are unevenly distributed.


In our case, a tweet can be reached only if at least one of the 64 emojis is used as a word, meaning it has to be a single character separated by blank space.

this kind of tweets is arguably cleaner, as it is often the case that this emoji is used to wrap up the whole post and clusters of repeated emojis are less likely to appear in such tweets.


The situation would be more serious is λ in Equation 7 is set higher.

this phenomenon does not impair the fluency of generated sentences, as can be seen in Figure 5.


Mathematically, it is analytically proven that α = 0.5 for an i.i.d process, and the proof is included as Supplementary Material.

α = 1.0 when all segments always contain the same proportion of the elements of W .


Therefore, Taylor exponent can reasonably serve for evaluating machinegenerated text.

to character-level neural language models, neural-network-based machine translation (NMT) models are, in fact, capable of maintaining the burstiness of the original text.


However, implementations usually proceed by some kind of iterative probabilistic node-pair selection process, and in this way suffer from the same statistical pitfalls as swarm frameworks.

to swarm models, interaction is rigidly restricted to immediately connected nodes, so to achieve gradient interaction probabilities, edges must be frequently updated or nearly fully-connected graphs with carefully assigned edge weights would need to be constructed and motivated.


This is a verb phrase in the sentence "In the above figure, x is prime and 2x = 3y."

it is a noun phrase in the sentence "The equation 2x = 3y has 2 solutions."


One therefore would not expect much, if any, performance gain from infusion of sentiment information.

such infusion should not subtract or harm the quality of word embeddings either.


(Mohammad et al., 2016) is a metaphorical expression.

when the sentence is parsed into a verbdirect object phrase, climb ladder, it appears literal.


Taking Skip-gram for example, empirically, input vectors of words with the same POS, occurring within the same contexts tend to be close in the vector space (Mikolov et al., 2013), as they are frequently updated by back propagating the errors from the same context words.

input vectors of words with different POS, playing different semantic and syntactic roles tend to be distant from each other, as they seldom occur within the same contexts, resulting in their input vectors rarely being updated equally.


They constructed a Gaussian graphical model which can extrapolate continuous representations for unknown words.

these morphology-based models directly exploit the internal compositions of words to encode morphological regularities into word embeddings, and some by-products are also produced like morpheme embeddings.


It is expected that the hearing will go on for two days.

the Republican complainant in the House wanted to summon 15 people including Lewinsky to testify in court.


It can be seen all current tree-based NMT systems use only one tree for encoding or decoding.

we hope to utilize multiple trees (i.e., a forest).


The attributive phrase of "Czech border region" is a complete sentence.

the attributive is not allowed to be a complete sentence in Chinese.


For the example in Figure 4, we observed that for s2s model, the decoder paid attention to the word "Czech" twice, which causes the output sentence contains the Chinese translation of Czech twice.

for our forest model, by using the syntax information, the decoder paid attention to the phrase "In the Czech Republic" only once, making the decoder generates the correct output.


(2016), very recently, Zaremoodi and Haffari (2017) proposed a forest-based NMT method by representing the packed forest with a forest-structured neural network.

their method was evaluated in small-scale MT settings (each training dataset consists of under 10k parallel sentences).


The training of these methods is fast, because of the linear structures of RNNs.

all these syntax-based NMT systems used only the 1-best parsing tree, making the systems sensitive to parsing errors.


The first N − 1 layers are identical and represent the original layers of Trans-  Context encoder: The context encoder is composed of a stack of N identical layers and replicates the original Transformer encoder.

to related work (Jean et al., 2017;Wang et al., 2017), we found in preliminary experiments that using separate encoders does not yield an accurate model.


This confirms that the model does rely on context information to achieve the improvement in translation quality, and is not merely better regularized.

the model is robust towards being shown a random context and obtains a performance similar to the context-agnostic baseline.


Our model generates the target translation word-by-word from left to right, similar to the vanilla attentional neural translation model.

it conditions the generation of a target word not only on the previously generated words and the current source sentence (as in the vanilla NMT model), but also on all the other source sentences of the document and their translations.


To be able to fit the computational graph of the document NMT model within GPU memory limits, we pre-train the sentence-level bidirectional RNN using the language modelling training objective.

the document-level bidirectional RNN is trained together with other parameters of the document NMT model by back-propagating the document translation training objective.


We may attribute this to these specific datasets, that is documents from TED talks or European Parliament Proceedings may depend more on the local than on the global context.

for German→English , the target memory model performs the best show-  Table 6: Analysis of target context model.


It can be seen that the source sentence has the noun "Qimonda" but the sentencelevel NMT model fails to attend to it when generating the translation.

the single memory models are better in delivering some, if not all, of the underlying information in the source sentence but the dual memory model's translation quality surpasses them.


Until now, this knowledge was only used as part of simple disparate heuristics and manual disambiguation procedures.

it is possible to plot this spatial data on a world map, which can then be reshaped into a 1D feature vector, or a Map Vector, the geographic representation of location mentions.


Table 1 shows the effectiveness of this heuristic, which is competitive with many geocoders, even outperforming some.

the baseline is not effective on WikToR as the dataset was deliberately constructed as a tough ambiguity test.


We use the text data to identify activities that are potential goal-acts for a location.

we also need to identify locations and want to include both proper names (e.g., Disneyland) as well as nominals (e.g., store, beach), so Named Entity Recognition will not suffice.


When adding Activity Similarity into the algorithm, we find that A L slightly improves performance, but A O and A E do not.

we also tried combining them and obtained improved results by using A L and A E together, yielding an MRR P score of 0.42.


And "phone" arguably is not a location at all, but most human annotators treated it as a virtual location, listing goal-acts related to telephones.

our algorithm considered phones to be similar to computers, which makes sense for today's smartphones.


First, we have the Marginal Popularity score and Conditional Popularity score as two context-independent features, which could compensate for each other.

as discussed in the previous section, some popular public meanings (e.g., "Artificial Intelligence") can be rarely mentioned in enterprise corpus by their full names, therefore both their marginal popularity score and conditional popularity score can be very low.


We also want to compare our method with the state-of-the-art Entity Linking (EL) systems based on public knowledge bases such as Wikipedia.

it is unfair to directly compare as most enterprise specific meanings are unknown to them.


Recently, there have been a few works (Jain et al., 2007;Larkey et al., 2000;Nadeau and Turney, 2005;Taneva et al., 2013) on automatically mining acronym meanings by leveraging Web data (e.g., query sessions, click logs).

it is hard to apply them directly to enterprises, since most data in enterprises are raw text and therefore the query sessions/click logs are rarely available.


It requires all TempRels between GENERIC 3 and NON-GENERIC events to be labeled as vague, which conceptually models the overall structure by two disjoint time-axes: one for the NON-GENERIC and the other one for the GENERIC.

as shown by Examples 1-3 in which the highlighted events are NON-GENERIC, the TempRels may still be ill-defined: In Example 1, Serbian police tried to restore order but ended up with conflicts.


tended the conference, the projection of submit a paper onto the main axis is clearly before attended.

this projection requires strong external knowledge that a paper should be submitted before attending a conference.


Since Asian crisis is on the main axis, it seems that e4:hardest hit is on the main axis as well.

the "hardest hit" in "Asian crisis before hardest hit" is only a projection of the original e4:hardest hit onto the real axis and is valid only when this OPINION is true.


With the availability of large datasets and the recent progress made by neural methods, variants of sequence to sequence learning (seq2seq) (Sutskever et al., 2014) architectures have been successfully applied for building conversational systems (Serban et al., , 2017b.

despite these methods being the stateof-the art frameworks for conversation generation, they suffer from problems such as lack of diversity in responses and generation of short, repetitive and uninteresting responses (Liu et al., 2016;Serban et al., , 2017b.


Tech Support dataset contains conversations pertaining to an employee seeking assistance from an agent (technical support) -to resolve problems such as password reset, software installation/licensing, and wireless access.

to Ubuntu dataset, this dataset has clearly two distinct users -employee and agent.


Table 4 compares our model with other recent generative models (Serban et al., 2017a) -LSTM (Shang et al., 2015), HRED ) & VHRED (Serban et al., 2017b).We do not compare our model with Multi-Resolution RNN (MRNN) (Serban et al., 2017a), as MRNN explicitly utilizes the activities and entities during the generation process.

the proposed EED model and the other models used for comparison are agnostic to the activity and entity information.


Such systems allow users to formulate questions with greater flexibility.

although state-of-the-art systems have achieved a high accuracy of 80% to 90% (Dong and Lapata, 2016) on well-curated datasets like GEO (Zelle and Ray, 1996) and ATIS (Zettlemoyer and Collins, 2007), the best accuracies on datasets with questions formulated by real human users, e.g., WebQuestions (Berant et al., 2013), GraphQuestions , and WikiSQL (Zhong et al., 2017), are still far from enough for real use, typically in the range of 20% to 60%.


(2016) incorporate coarse-grained user interaction, i.e., asking the user to verify the correctness of the final results.

for real-world questions, it may not always be possible for users to verify result correctness, especially in the absence of supporting evidence.


(2017) have shown that incorporating fine-grained user interaction can greatly improve the accuracy of NLIDBs.

they require that the users have intimate knowledge of SQL, an assumption that does not hold for general users.


Metric validation in Grammatical Error Correction (GEC) is currently done by observing the correlation between human and metric-induced rankings.

such correlation studies are costly, methodologically troublesome, and suffer from low inter-rater agreement.


We test this hypothesis by computing the average difference in GLEU score between all pairs in the sampled chains, and find it to be slightly negative (-0.00025), which is in line with GLEU's small negative τ .

plotting the GLEU scores of the originals grouped by the number of errors they contain, we find they correlate well ( Figure 5), indicating that GLEU performs well in comparing the quality of corrections of different sentences.


As we can see in Section 4, ensemble indeed improves the performance of baseline models.

real world deployment is usually constrained by computation and memory resources.


In Section 4.2, improvements from distilling the ensemble have been witnessed in both the transition-based dependency parsing and neural machine translation experiments.

questions like "Why the ensemble works better?


When humans comprehend a natural language sentence, they arguably do it in an incremental, left-to-right manner.

when humans consciously annotate a sentence with syntactic structure, they rarely ever process in fixed left-to-right order.


xcomp This is an instance of a general principle that, if there is a shortening of an MAE multiword phrase into a single word, the annotations on that word should mirror the edges in and out of the original phrase's subgraph (as in 's fudge expressions).

in contrast to the UD Treebank, we did not attempt to split up these words into their component words (e.g.


As before, we observed significant increases in accuracy moving from the Morpho-Tagger to the ARK Tagger settings.

neither adding embeddings nor synthetic data appeared to significantly increase accuracy for these features.


3) to calculate the probability distribution over V i.e., P g j (v) where v ∈ V , and then choose the token with the highest generation probability.

in our case, tokens in the target sequence Y might be exactly copied from the input X (e.g., "Italian").


It helps to reduce search space because all key information of X for task completion has been included in B t .

to recent work (Eric and Manning, 2017a) that also employs a copy-attention mechanism to generate a knowledge-base search API and machine responses, our proposed method advances in two aspects: on one hand, bspans reduce the search space from U 1 R 1 ...U t R t to B t−1 R t−1 U t by compressing key points for the task completion given past dialogues; on the other hand, because bspans revisit context by only handling the B t with a fixed length, the time complexity of TSCP is only O(T ), comparing O(T 2 ) in (Eric and Manning, 2017a).


This approach is also referred to as Language Model Type condition (Wen et al., 2016b) The standard cross entropy is adopted as our objective function to train a language model: In response generation, every token is treated equally.

in our case, tokens for task completion are more important.


Previous work predefines all slot values in a belief tracker.

a user may request new attributes that has not been predefined as a classification label, which results in an entity mismatch.


On the one hand, these specialized embeddings are more difficult to obtain than word embeddings from language modeling.

these embeddings are not specific to any dialogue domain and are directly usable for new domains.


One can find that in (a) hop 1, there is no clear separation of two different colors but each of which tends to group together.

the separation becomes clearer in (b) hop 6 as each color clusters into several groups such as location, cuisine, and number.


In addition, Ptr-Unk often cannot copy the correct token from the input, as shown by "PAD" in Table 1.

mem2Seq is able to produce the correct responses in this two examples.


Reinforcement Learning (Ranzato et al., 2016), Beam Search (Wiseman and Rush, 2016)) to improve both responses relevance and entity F1 score.

we preferred to keep our model as simple as possible in order to show that it works well even without advanced training methods.


(Li et al., 2016a) proposed a mutual information model(MMI) to tackle this problem.

it is not a unified training model, instead it still trained original Seq2Seq model, and used the Maximum Mutual Information criterion only for testing to rerank the primary top-n list.


With these latent embedding in the mid of Seq2Seq, the mechanism-aware Seq2Seq can generate different mechanism responses.

most of these models are using an averaged approach for optimization, similar to that in Seq2Seq.


We can see that Mechanism produces responses with the same meaning, such as 'Wade is so amazing' and 'It is really good'.

our CVaR models give specific responses with diverse meanings.


End-to-end learning framework is useful for building dialog systems for its simplicity in training and efficiency in model updating.

current end-to-end approaches only consider user semantic inputs in learning and under-utilize other user information.


Because traditional modular-based systems are harder to train, to update with new data and to debug errors, end-to-end trainable systems are more popular.

no work has tried to incorporate sentiment information in the end-to-end trainable systems so far to create sentiment-adaptive systems that are easy to train.


building a user simulator is not a trivial task.


Many previous RL studies used delayed rewards, mostly task success.

delayed rewards make the converging speed slow, so some studies integrated estimated per-turn immediate reward.


One possible reason is that a total of eight dialogic features were added to the model, and some of them might contain noises and therefore caused the model to overfit.

using predicted sentiment information as an extra feature, which is a more condensed information, outperformed the other models both in terms of turn-level F-1 score and dialog accuracy which indicates if all turns in a dialog are correct.


SAMPLE performs best for CHAR: best results in five out of eight cases.


The transfer or share of knowledge between languages is a popular solution to resource scarcity in NLP.

the effectiveness of cross-lingual transfer can be challenged by variation in syntactic structures.


On one hand, Latin would display the attribute-value pairs TENSE=FUTURE and CASE=DATIVE among the features of erit and superis.

in English the function words (will and to) add nodes to the dependency structure, modifying the equivalent words (be and gods).


However, they have also weaknesses: the Jaccard index of feature sets is not reliable for languages with a limited number of morphologically expressed grammatical categories.

the tree edit distance measure requires resources (such as treebanks and parallel corpora) that are not available for many languages.


This implies that the amount of data required would not just be twice, but probably 10 or 100 times more than that for training a monolingual system with similar accuracy.

apart from user-generated content on the Web and social media, it is extremely difficult to gather large volumes of CM data because (a) CM is rare in formal text, and (b) speech data is hard to gather and even harder to transcribe.


3), in the EC model, a pair of monolingual parallel tweets gives rise to a large number (typically exponential in the length of the tweet) of CM tweets.

in reality, only a few of those tweets would be observed.


We find that with proper representation settings, the same conclusion holds for neural NER.

lattice LSTM is a better choice compared with both word LSTM and character LSTM.


A CNN representation of character sequences gives a slightly higher F1-score compared to LSTM character representations.

further using character bigram information leads to increased F1-score over word+char LSTM, but decreased F1-score over word+char CNN.


The quality of the lexicon may affect the accuracy of our NER model since noise words can potentially confuse NER.

our lattice model can potentially learn to select more correct words during NER training.


There has been significant amount of work on Semantic Role Labeling (Lang and Lapata, 2011;Titov and Khoddam, 2015;Roth and Lapata, 2016), which can be considered as nary relation extraction.

we are interested in inducing the schemata, i.e., the type signature of these relations.


For example, a prediction that a city is in France might depend on the conjunction of several facets of textual evidence linking the city to the French language, the Euro, and Norman history.

the common maximum aggregation approach is to move the final prediction layer to the sentence-to-vector modules and then aggregate by max-pooling the sentence level predictions.


Large KBs and corpora are needed to train KBP systems in order to collect enough mentions for each relation.

most of the existing Knowledge Base Population tasks are small in size (e.g.


In principle, instead of relying on the linear combination of relation embeddings matrices R k , we could directly predict a context-specific relation embedding where g is a neural network.

in preliminary experiments we observed that this resulted in overfitting and poor performance.


Anonymization gives comparable overall performance gains on our graph-to-sequence model as the copy mechanism (comparing Graph2seq+Anon with Graph2seq+copy).

the copy mechanism has several advantages over anonymization as discussed in Section 3.5.


Though directly connected in the original graph, their distance in the serialization result (the input of S2S) is 26, which may be why S2S makes these mistakes.

g2S handles "a / account" and "o / old" correctly.


In the training instances, serialized nodes that are close to each other can originate from neighboring graph nodes, or distant graph nodes, which prevents the decoder from confidently deciding the correct relation between them.

g2S sends the node "p / provide" simultaneously with relation "ARg0" when calculating hidden states for "a / agree", which facilitates the yielding of "the agreement provides".


This representation allows easy data share between KBs.

usually the elements of a triple are stored as Uniform Resource Identifiers (URIs), and many predicates (words or phrases) are not intuitive; this representation is difficult to comprehend by humans.


The adapted standard triple encoder has an advantage in preserving the intra-triple relationship.

it has not considered the structural rela- tionships between the entities in different triples.


Both models have a predefined relationship between the vertices (Graph LSTM uses spatial relationships: top, bottom, left, or right between super-pixels; Tree LSTM uses dependencies between words in a sentence as the relationship).

a KB has an open set of relationships between the vertices (i.e., the predicate defines the relationship between entities/vertices) which make our problem more difficult to model.


Language models based on Recurrent Neural Networks (RNNs) have brought substantial advancements across a wide range of language tasks (Jozefowicz et al., 2016;Bahdanau et al., 2015;Chopra et al., 2016).

when used for longform text generation, RNNs often lead to degenerate text that is repetitive, self-contradictory, and overly generic, as shown in Figure 1.


Repetition can be reduced by prohibiting recurrence of the trigrams as a hard rule (Paulus et al., 2018).

such hard constraints do not stop RNNs from repeating through paraphrasing while preventing occasional intentional repetition.


The RNN language model decomposes into per-word probabilities via the chain rule.

in order to allow for more expressivity over long range context we do not require the discriminative model scores to factorize over the elements of y, addressing a key limitation of RNNs.


The absolute performance of all the evaluated systems on BLEU and Meteor is quite low (Table 1), as expected.

in relative terms L2W is superior or competitive with all the baselines, of which ADAPTIVELM performs best.


Our goal is to generate a sentence containing the target word.

vanilla seq2seq model cannot guarantee the target word to appear in the generated sequence all the time.


Both descriptions are true, but the latter is most salient given the player's goal.

sometimes, none of the aspects may stand out as being most salient, and the most salient aspect may even change from commentator to commentator.


Hence, RAML enjoys a much more stable optimization without the need of pretraining.

in order to optimize the RAML objective (Eqn.


MS-MARCO uses web queries as questions and the answers are synthesized by workers from documents relevant to the query.

in most cloze-style datasets (Mostafazadeh et al., 2016;Onishi et al., 2016) the questions are created automatically by deleting a word/entity from a sentence.


This is indeed true for the SQuAD, TriviaQA and NewsQA datasets.

in our dataset, in many cases the answers do not correspond to an exact span in the document but are synthesized by humans.


Note that (Tan et al., 2017) recently proposed an answer generation model for the MS MARCO dataset.

the authors have not released their code and therefore, in the interest of reproducibility of our work, we omit incorporating this model in this paper.


(6) From a pair of begin and end points, the answer string can be extracted from the passage.

rather than output the results (start/end points) from the final step (which is fixed at T − 1 as in Memory Networks or dynamically determined as in ReasoNet), we utilize all of the T outputs by averaging the scores: is a multinomial distribution over {1, .


(2018a) combined a ranker for selecting the relevant passage and a reader for producing the answer from it.

this approach only depended on one passage when producing the answer, hence put great demands on the precisions of both components.


On SearchQA, we find that our extraction model alone performs not that well compared with the state-of-the-art model without re-rankers.

the improvement brought by our selection model isolatedly or jointly trained still demonstrates the importance of our two-stage framework.


Taking K = 1 degrades the performance, which conforms to the expectation, as the correct candidates become less in this stricter situation.

taking K = 3 can not improve the performance further.


In the last example, MINIMAL fails to answer the question, since the inference over first and second sentences is required to answer the question.

selected sentence in 1883-84 Germany began to build a colonial empire in Africa and the South Pacific, before losing interest in imperialism.


propose a DS-QA system, which retrieves relevant texts of the question from a large-scale corpus and then extracts answers from these texts using reading comprehension models.

the retrieved texts in DS-QA are always noisy which may hurt the performance of DS-QA.


Therefore, simply using all retrieved paragraphs equally to extract answer may bring in much noise.

our+FULL model still has a slight improvement by considering the confidence of each retrieved paragraph.


As Table 7 shows, if we remove L adv , the translation performance decreases by 0.64 BLEU point.

when L noisy is excluded from the training objective function, it results in a significant drop of 1.66 BLEU point.


(2016) extend the attentional model to include structural biases from word based alignment models, including positional bias, Markov conditioning, fertility and agreement over translation directions.

we did not delve into the attention model or sought to redesign it in our new bridging proposal.


These approaches are somewhat similar to our source-side bridging model.

inspired by the insight of shortening the distance between source and target embeddings in the seq2seq processing chain, in the present paper we propose more strategies to bridge source and target word embeddings and with better results.


quality ratings, is related to the task of sentence-level quality estimation (sQE).

there are crucial differences between sQE and the reward estimation in our work: sQE usually has more training data, often from more than one machine translation model.


(2018) change the auto-regressive architecture to speed up translation by directly generating target words without relying on any previous predictions.

compared with our work, their model achieves the improvement in decoding speed at the cost of the drop in translation quality.


While the rule-based semantic parser has high precision and gauges the amount of structural variance in the data, it cannot generalize beyond observed examples.

we can automatically generate non-abstract utterance-program pairs from the manually annotated abstract pairs and train a semantic parser with strong supervision that can potentially generalize better.


3 The resulting parser can be used as a standalone semantic parser.

it can also be used as an initialization point for the weakly-supervised semantic parser.


2016proposed a user interface for the Freebase database that enables a fast and easy creation of parses.

in their setup the worker still requires expert knowledge about the Freebase database, whereas in our approach feedback can be collected freely and from any user interacting with the system.


Consequently the number of tokens and types increase in a similar vein.


Because they are graphs and not trees, they can capture reentrant semantic relations, such as those induced by control verbs and coordination.

it is technically much more challenging to parse a string into a graph than into a tree.


The same AM dependency tree may represent multiple indexed AM terms, because the order of apply and modify operations is not specified in the dependency tree.

it can be shown that all well-typed AM terms that map to   Fig.


Stochastic Gradient Descent (SGD) with negative sampling is the most prevalent approach to learn word representations.

it is known that sampling methods are biased especially when the sampling distribution deviates from the true data distribution.


By far, most state-of-the-art embedding methods rely on SGD and negative sampling for optimization.

the performance of SGD is highly sensitive to the sampling distribution and the number of negative samples (Chen et al., 2018;Yuan et al., 2016), as shown in Figure 1.


To address the above-mentioned limitations of SGD, a natural solution is to perform exact (full) batch learning.

to SGD, batch learning does not involve any sampling procedure and computes the gradient over all training samples.


hibits a more stable convergence due to its full batch learning.

gloVe has a more dramatic fluctuation because of the one-sample learning scheme.


In other words, the parallelization of SGD is not well suited to a large number of work- ers.

the parameter updates in AllVec are completely independent of each other, therefore AllVec does not have the update collision issue.


In this paper, we presented AllVec, an efficient batch learning based word embedding model that is capable to leverage all positive and negative training examples without any sampling and approximation.

with models based on SGD and negative sampling, AllVec shows more stable convergence and better embedding quality by the all-sample optimization.


Amos and Kolter (2017) extend their efforts to a class of subdifferentiable quadratic programs.

they both require that the intermediate objective has an invertible Hessian, limiting their application in NLP.


The standard protocol for obtaining a labeled dataset is to have a human annotator view each example, assess its relevance, and provide a label (e.g., positive or negative for binary classification).

this only provides one bit of information per example.


They use crowdsourcing to craft passage perturbations intended to fool the network, and then query the network to test their effectiveness.

we propose improving the analysis of question answering systems.


We analyze that this is because the models share the same encoding and matching layers at bottom level and this relevance guarantees that the content and verification models will not violate the boundary model too much.

we also see that the verification score can really make a difference here when the boundary model makes an incorrect decision among the confusing answer candidates ([1], [3], [4], [6]).


For example, Russian and Arabic are morphologically-rich languages and heavily utilize grammatical markers to indicate grammatical as well as semantic functions.

chinese, as an analytic language, encodes grammatical and semantic information in a highly configurational rather than either inflectional or derivational way.


As explained in Section 1, we assume that predicting the movement on trading day d can benefit from predicting the movements on its former trading days.

due to the general principle of sample independence, building connections directly across samples with temporally-close target dates is problematic for model training.


The comparison with INDEPENDENTANALYST also shows the effectiveness of capturing temporal dependencies between predictions with the temporal auxiliary.

the effects of the temporal auxiliary are more complex and will be analyzed further in the next section.


This under utilizes the propagation information due to such oversimplified treatment of tree structure.

sVM-TK is an integrated kernel and can fully utilize the structure by comparing the trees based on both textual and structural similarities.


It is not easy to tell if each Modern Baseball refers to a name or not from the textual evidence only.

using the associated images as reference, we can easily infer that Modern Baseball in the first sentence should be the name of a band because of the implicit features from the objects like instruments and stage, and the Modern Baseball in the second sentence refers to the sport of baseball because of the pitcher in the image.


The BLSTM-CRF sequence labeling model benefits from using the visual context vector to initialize the LSTM cell.

the better way to utilize visual features for sequence labeling is to incorporate the features at word level individually.


A number of variants of RNNs such as Long Short-Term Memory networks (LSTM; Hochreiter and Schmidhuber, 1997) and Gated Recurrent Unit networks (GRU; Cho et al., 2014) have been designed to model text capturing long-term dependencies in problems such as language modeling.

document modeling, a key to many natural language understanding tasks, is still an open challenge.


As one might imagine, HUMAN gets ranked 1st most of the time (41%).

it is closely followed by XNET which ranked 1st 28% of the time.


For the SQuAD dataset, the results are comparable (less than 1%).

the improvement for WikiQA reaches ∼3% and then the gap shrinks again for NewsQA, with an improvement of ∼1%.


For example, the neural variational document model (NVDM;Miao et al., 2016) allows θ i ∈ R K and achieves normalization by taking the softmax of θ i B.

the experiments in Srivastava and Sutton (2017) found the performance of the NVDM to be slightly worse than LDA in terms of perplexity, and dramatically worse in terms of topic coherence.


In these recent works, words are represented as a vector of discrete numbers, which are very efficient storage-wise, while showing comparable performance on several NLP tasks, relative to continuous word embeddings.

discrete representations that are learned in an endto-end manner at the sentence or document level have been rarely explored.


Most of the previous text hashing methods focus on modeling the encoding distribution p(z|x), or hash function, so the local/global pairwise similarity structure of documents in the original space is preserved in latent space (Zhang et al., 2010;Wang et al., 2013;Xu et al., 2015;Wang et al., 2014).

the generative (decoding) process of reconstructing x from binary latent code z, i.e., modeling distribution p(x|z), has been rarely considered.


Previous studies have shown syntactic information has a remarkable contribution to SRL performance.

such perception was challenged by a few recent neural SRL models which give impressive performance without a syntactic backbone.


Syntactic information plays an informative role in semantic role labeling.

few studies were done to quantitatively evaluate the syntactic contribution to SRL.


Evaluation Measure For SRL task, the primary evaluation measure is the semantic labeled F 1 score.

the score is influenced by the quality of syntactic input to some extent, leading to unfaithfully reflecting the competence of syntax-based SRL system.


Differently,  proposed a syntax-agnostic model using effective word representation for dependency SRL, which for the first time achieves comparable performance as stateof-the-art syntax-aware SRL models.

most neural SRL works seldom pay much attention to the impact of input syntactic parse over the resulting SRL performance.


, (s m i , a m i ) for each instructionx i .

the agent context, the information available to the agent at step k, is the execution up until but not including step k. to the world state, the agent context also includes instructions and the execution so far.


We believe this is due to the fewer number of RE related parameters and the shorter distance that the gradient needs to travel from the loss to these parameters -both make logit easier to train.

since logit directly modifies the output, the final prediction is more sensitive to the insufficiently trained weights in logit, leading to the inferior results in the 5-shot setting.


We note that the sequence-level smoothing tend to generate lengthy captions overall, which is maintained in the combination.

the token-level smoothing allows for a better recognition of objects in the image that stems from the robust training of the classifier e.g.


UNK, and does not reflect the smoothness of the underlying continuous distribution of certain attributes.

are both grammatical and realistic, as in this example: this maps all unseen numerals to the same unknown type and ignores the smoothness of continuous attributes, as shown in Figure 1.


Several tasks such as information extraction, question answering, and machine translation have benefited from them.

in their vanilla forms, these networks are constrained by the sequential order of tokens in a sentence.


To mitigate this limitation, structural (dependency or constituency) information in a sentence was exploited and witnessed partial success in various tasks (Goller and Kuchler, 1996;Yamada and 1 Our code for experiments on the SICK dataset is publicly available at https://github.com/amulyahwr/ acl2018 Knight, 2001;Quirk et al., 2005;Socher et al., 2011;Tai et al., 2015).

alignment techniques (Brown et al., 1993) and attention mechanisms (Bahdanau et al., 2014) act as a catalyst to augment the performance of classical Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) models, respectively.


Distant supervision has become the standard method for relation extraction.

even though it is an efficient method, it does not come at no cost-The resulted distantly-supervised training samples are often very noisy.


To suppress the noisy (Roth et al., 2013), recent stud-ies (Lin et al., 2016) have proposed the use of attention mechanisms to place soft weights on a set of noisy sentences, and select samples.

we argue that only selecting one example or based on soft attention weights are not the optimal strategy: To improve the robustness, we need a systematic solution to make use of more instances, while removing false positives and placing them in the right place.


Recently, with the advance of neural network techniques, deep learning methods (Zeng et al., 2014(Zeng et al., , 2015 are introduced, and the hope is to model noisy distant supervision process in the hidden layers.

their approach only selects one most plausible instance per entity pair, inevitably missing out a lot of valuable training instances.


Distant supervision relation extraction is to predict the relation type of entity pair under the automatically-generated training set.

the issue is that these distantly-supervised sentences that mention this entity pair may not express the desired relation type.


First, the prerequisite of reinforcement learning is that the external environment should be modeled as a Markov decision process (MDP).

the traditional setting of relation extraction cannot satisfy this condition: the input sentences are independent of each other.


Intuitively, a relation can be modeled by a matrix mapping entity vectors.

relations reside on low dimension sub-manifolds in the parameter space of arbitrary matrices-for one reason, composition of two relations M 1 , M 2 may match a third M 3 (e.g.


the composition of currency of country and country of film matches the relation currency of film budget), which could be captured by modeling composition in a space.

modeling relations as mappings naturally requires more parameters -a general linear map between d-dimension vectors is represented by a matrix of d 2 parameters -which are less likely to be shared, impeding transfers of facts between similar relations.


Our work inherits the same motivation with ITransF in terms of promoting parameter-sharing among relations.

the base model used in this work originates from RESCAL (Nickel et al., 2011), in which relations are naturally represented as analogue to the adjacency matrices (Sec.2).


It is also used for pretraining other deep neural networks (Erhan et al., 2010).

when combined with other models, the learning of autoencoders, or more generally sparse codings (Rubinstein et al., 2010), is usually conveyed in an alternating manner, fixing one part of the model while optimizing the other, such as in Xie et al.


WN18 collects word relations from WordNet (Miller, 1995), and FB15k is taken from Freebase (Bollacker et al., 2008); both have filtered out low frequency entities.

it is reported in Toutanova and Chen (2015) that both WN18 and FB15k have information leaks because the inverses of some test triples appear in the training set.


Many supervised deep models have been proposed for this problem (Liu et al., 2015;Yin et al., 2016;Wang et al., 2017), and obtained promising results.

these methods fail to adapt well across domains, because the aspect terms from two different domains are usually disjoint, e.g., laptop v.s.


On one hand, these previous attempts have verified that syntactic information between words, which can be used as a bridge between domains, is crucial for domain adaptation.

dependency-tree-based RNN (Socher et al., 2010) has proven to be effective to learn high-level feature representation of each word by encoding syntactic relations between aspect terms and opinion terms (Wang et al., 2016).


Obviously, the performance of RNSCN-GRU without an autoencoder significantly deteriorates when the auxiliary labels are very noisy.

rNSCN + -GrU (r) achieves acceptable results compared to rNSCN + -GrU.


This may not greatly influence opinion extraction, as shown, because the two domains usually share many common opinion terms.

the significant difference in aspect terms makes the learning more dependent on common relations.


Learning policies for task-completion dialogue is often formulated as a reinforcement learning (RL) problem (Young et al., 2013;Levin et al., 1997).

applying RL to real-world dialogue systems can be challenging, due to the constraint that an RL learner needs an environment to operate in.


Most of recent studies in this area have adopted this strategy (Su et al., 2016a;Lipton et al., 2016;Zhao and Eskenazi, 2016;Williams et al., 2017;Dhingra et al., 2017;Liu and Lane, 2017;Peng et al., 2017b;Budzianowski et al., 2017;Peng et al., 2017a).

user simulators usually lack the conversational complexity of human interlocutors, and the trained agent is inevitably affected by biases in the design of the simulator.


4 For simplicity, we assume some random initial state vectors such as f C 0 and b C |w i |+1 when we describe LSTMs.

we also want access to reliable probability estimates instead of raw scores -we accomplish this by constructing a custom loss function.


It is hard to assign this sentence happy given only the text attention.

the acoustic attention focuses on "you're" and "west-sider", removing emphasis from "don't" and "like".


(ii) The word alignment can be easily applied to human speech.

it is difficult to align the visual information with text, especially if the text only describes the video or audio.


The distribution shows a natural skew towards more frequently used emotions.

the least frequent emotion, fear, still has 1,900 data points which is an acceptable number for machine learning studies.


ing that the DFG is able to find useful information in unimodal, bimodal and trimodal interactions.

in cases (II) and (III) where the visual modality is either uninformative or contradictory, the efficacies of v → l, v and v → l, a, v and l, a → l, a, v are reduced since no meaningful interactions involve the visual modality.


Subsequently, DFG gives low values to efficacies that rely unilaterally on language or audio alone: the (l → τ ) and (a → τ ) efficacies seem to be consistently low.

the visual modality appears to have a partially isolated behavior.


Previous research in this field has exploited the expressiveness of tensors for multimodal representation.

these methods often suffer from exponential increase in dimensions and in computational complexity introduced by transformation of input into tensor.


(2017) proposes a low-rank tensor-based fusion framework to improve the face recognition performance using the fusion of facial attribute information.

none of these previous work aims to apply low-rank tensor techniques for multimodal fusion.


In the simplest case, this score can simply be the log-likelihood log p SCG (v) of the story-version, according to the Sequential-CG model.

this is problematic since this is biased towards choosing shorter endings.


Because a cause must occur earlier than its effect, temporal and causal relations are closely related and one relation often dictates the value of the other.

limited attention has been paid to studying these two relations jointly.


In Example 2, it is unclear whether the government stifled people because people raged, or people raged because the government stifled people: both situations are logically reasonable.

if we account for the temporal relation (that is, e4:stifle happened before e3:raged), it is clear that e4:stifle is the cause and e3:raged is the effect.


Note that the temporal performance in Table 4 is consistently better than those in Table 2 because of the higher IAA in the new dataset.

the improvement brought by joint reasoning with causal relations is the same, which further confirms the capability of the proposed approach.


It is unrealistic to manually identify a lot of positive examples for each possible category.

new information needs indeed emerge everywhere in many real-world scenarios.


However, the scalar information like cosine similarity between two embedding vectors is too coarse or limited to reflect the conceptual relevance.

we believe that the embedding features could provide rich knowledge towards the conceptual relevance.


The embedding offsets are used in deriving word semantic hierarchies in (Fu et al., 2014).

there is no existing work incorporating these two kinds of feature interactions for relevance estimation.


In GLU, both the convolution operation and the gates share the same input X.

in this work, we aim to identify which filters capture the relevance signals in a category-dependent manner.


Specifically, from Table 6, we can see that both the element-wise subtraction and element-wise product play equally on Movie Review dataset.

it is observed that DAZER experiences significantly a much larger performance degradation on 20NG dataset.


RNN can model the whole sequence and capture long-term dependencies (Chung et al., 2014).

modeling the entire sequence sometimes case1: One of the seven great unsolved mysteries of mathematics may have been cracked by a reclusive Russian.


Table 1: Examples of topic classification can be a burden, and it may neglect key parts for text categorization (Yin et al., 2017).

cNN is able to extract local and position-invariant features well (Scherer et al., 2010;collobert et al., 2011).


We find attentive pooling is not significantly affected by window sizes.

the performance of mean pooling becomes worse as the window becomes larger.


For AG and DBPedia, the optimal window size is 15.

for Yelp P. the optimal window size is 40 or even larger.


(Das et al., 2015) proposed a new technique for topic modeling by treating the document as a collection of word embeddings and topics itself as multivariate Gaussian distributions in the embedding space.

the assumption that topics are unimodal in the embedding space is not appropriate, since topically related words can occur distantly from each other in the embedding space.


It requires sentences within the text to be interpreted, by themselves, as well as with other sentences in the text (Van Dijk, 1980).

cohesion makes use of Example Comments My favourite colour is blue.


the text makes sense due to a lot of implicit clues (blue, favourite, relaxing, look up (and see the blue sky)).


(2010) (using machine learning) and Taghipour (2017) (using neural networks).

there has been a lot of work done to model coherence and cohesion, using methods like lexical chains (Somasundaran et al., 2014), an entity grid (Barzilay and Lapata, 2005), etc.


Correct Answer: "93" (44+49) 14 We also used other classifiers, like Naive Bayes, Logistic Regression and Random Forest.


As the size of the training set increases, the CER of our model decreases consistently for both single and multiple input correction on the RDD newspapers.

the performance curve of correction model on TCP books dataset is flatter since it is larger overall than RDD newspapers.


Libovický and Helcl (2017) explore different attention combination strategies for multiple information sources such as image and text.

our method does not require multiple inputs for training, and the attention combination strategies are used to integrate multiple inputs when decoding.


These models have improved the language generation tasks to a great extent, e.g., (Mikolov et al., 2010;Galley et al., 2015).

while generating text or code with a large number of named entities (e.g., different variable names in source code), these models often fail to predict the entity names properly due to their wide variations.


The closest work in this line is hierarchical neural language models (Morin and Bengio, 2005), which model language with word clusters.

their approaches do not focus on dealing with named entities as our model does.


A forward directional LSTM starts from the beginning of a sentence and goes from left to right sequentially until the sentence ends, and vice versa.

our approach is general and can be applied with other types of language models.


To investigate these questions, we provide all the full project information to SLP-Core (Hellendoorn and Devanbu, 2017) corresponding to our train set.

at test-time, to establish a fair comparison, we consider the perplexity metric for the same methods.


These approaches involve downgrading hyperlinks and inevitably omit certain information in hyper-docs.

no previous work investigates the information loss, and how it affects the performance of such downcasting-based adaptations.


It has shown promising results on feature-based ranking systems.

neural-IR leverages distributed representations and neural networks to learn more sophisticated ranking models form large-scale training data.


Similarly non-neural textual entailment models have been developed that incorporate knowledge bases.

these also require model-specific engineering (Raina et al., 2005;Silva et al., 2018).


It consists of automatically recommending sememes for words, which is expected to improve annotation efficiency and consistency.

existing methods of lexical sememe prediction typically rely on the external context of words to represent the meaning, which usually fails to deal with low-frequency and out-ofvocabulary words.


(1) The performances of SPSE, SPWE, and SPWE + SPSE decrease dramatically with low-frequency words compared to those with high-frequency words.

the performances of SPWCF, SPCSE, and SP-WCF + SPCSE, though weaker than that on highfrequency words, is not strongly influenced in the long-tail scenario.


Humans can intuitively conclude that clock + craftsman → clockmaker.

the external model does not per-form well for this example.


We find that one of the 100 pairs -'good' and 'bad' -shows the highest AUC (92.4).

another random pair ('happy' and 'evil') results in the worst performance with the AUC of 67.2.


Machine translation and bilingual word embeddings provide some relief through cross-lingual sentiment approaches.

they either require large amounts of parallel data or do not sufficiently capture sentiment information.


Machine translation (MT) can provide a way to transfer sentiment information from a resource-rich to resourcepoor languages (Mihalcea et al., 2007;Balahur and Turchi, 2014).

mT-based methods require large parallel corpora to train the translation system, which are often not available for underresourced languages.


Given the simple classification strategy, all models perform relatively well on phrases with negation (all reach nearly 60 F 1 in the binary setting).

while BLSE performs the best on negation in the binary setting (82.9 F 1 ), it has more problems with negation in the 4-class setting (36.9 F 1 ).


Thus, they should have similar embeddings across domains.

domain-specific word embeddings represent the fact that the sentiments or meanings across domains are different.


For example, the domain-common word "good" has high probability to be positive in different reviews across multiple domains.

for the word "lightweight", it would be positive in the electronics domain, but negative in the movie domain.


3 is not feasible in practice, given that the computation cost is proportional to the size of Λ.

similar to the skip-gram model, we can rely on negative sampling to address this issue.


It proposed to extract the domain specific and the domain invariant representations, simultaneously.

it does not explored the usage of the domain specific information.


It first computes the alignment scores between context vectors and target vector; then carry out a weighted sum with the scores and the context vectors.

the context vectors have to encode both the aspect and sentiment information, and the alignment scores are applied across all feature dimensions regardless of the differences between these two types of information.


It has a convolutional layer generating aspect features via ReLU activation function, which controls the magnitude of the sentiment signals according to the given aspect information.

the sigmoid function in GTU and GLU has the upper bound +1, which may not be able to distill sentiment features effectively.


Hence, the figure confirms that our DM-MCNN approach is able to exploit and customize the provided sentiment weights for the target domain.

unlike the VADER data, our transfer learning approach results in multi-dimensional sentiment embeddings that can more easily capture multiple domains right from the start, thus making it possible to use them even without further fine-tuning.


There are already previous attempts to intricately combine both CNN and RNN (Zhou et al., 2016), resulting to a slower model.

hCWE resorts to combine them by simply concatenating the word vectors encoded from both CNN and RNN encoders, i.e.


e(w i , u, p) , has been proven to improve the performance of sentiment classifiers (Chen et al., 2016a).

this method assumes that the user and product vectors are always present.


Normally, a sentiment classifier transforms the final vector v up , usually in a linear fashion, into a vector with a dimension equivalent to the number of classes C. A softmax layer is then used to obtain a probability distribution y over the sentiment classes.

finally, the full model uses a crossentropy over all training documents D as objective function L during training, where y is the gold probability distribution: HCSC has a nice architecture which can be used to improve the training.


Studies have shown the positive impact of deliberation on the quality of several document types, such as scientific papers, research proposals, political reports, and Wikipedia articles, among others (Kraut et al., 2012).

deliberative discussions may fail, either by agreeing on the wrong action, or by reaching no agreement.


We then used this corpus and other data in (Al-Khatib et al., 2017) to identify patterns of strategies across different general topics.

to those two studies targeting monological texts, here we address argumentation strategies in dialogical texts.


Conceptual Captions consists of about 3.3M image, description pairs.

with the curated style of the COCO images, Conceptual Captions images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles.


For instance, they hallucinate "front of building" for the first image, "clock and two doors" for the second, and "birthday cake" for the third image.

conceptual-trained models do not seem to have this problem.


Past datasets have been limited to only a few high-resource languages and unrealistically easy translation settings.

we have collected by far the largest available dataset for this task, with images for approximately 10,000 words in each of 100 languages.


Their dataset is restricted to six high-resource languages and a small vocabulary of 557 English words.

we present results for over 260,000 English words and 32 foreign languages.


For example, only looking at the right lower region of the chest x-ray image (Figure 1) without accounting for other areas, we might not be able to recognize what we are looking at, not to even mention detecting the abnormalities.

the tags can always provide the needed high level information.


For the targeted caption method, we use the targeted caption S as the input of RNN.

for the targeted keyword method we no longer know the exact targeted sentence, but only require the presence of specified keywords in the final caption.


Similarly, Path Finding involves a search problem that requires simple spatial deductions such as 'A is east of B implies B is west of A'.

the questions in our datasets involve longer descriptions, more entities, and more relations; they are thus harder to answer with simple deductions.


Specifically, (Seo et al., 2015) combined information from textual questions and diagrams to build a model for solving SAT geometry questions.

our task is different as diagrams are not provided as part of the input, but are generated from the words/symbols themselves.


Moreover, supervised training of deep neural network mod-els needs a large number of training samples while many interesting applications require rapid learning from a small amount of data, which poses an even greater challenge to the supervised setting.

humans learn in a way very different from the supervised setting (Skinner, 1957;Kuhl, 2004).


The best permutation between hypotheses and reference labels in terms of cross-entropy is selected and used for backpropagation.

this method still requires reference labels in the form of senone alignments, which have to be obtained on the clean isolated sources using a single-speaker ASR system.


It was evaluated under the same evaluation data and metric as in (Isik et al., 2016) based on the WSJ corpus.

there are differences in the size of training data and the options in decoding step.


14.0 Several previous works have considered an explicit two-step procedure Isik et al., 2016;Chen et al., 2017Chen et al., , 2018.

with our work which uses a single objective function for ASR, they introduced an objective function to guide the separation of mixed speech.


(2017) trained a multi-speaker speech recognizer using permutation-free training without explicit objective function for separation.

with our work which uses an end-toend architecture, their objective function relies on a senone posterior probability obtained by aligning unmixed speech and text using a model trained as a recognizer for single-speaker speech.


7is still hard-it requires us to normalize the distribution p θ , which, in turn, involves a sum over all lemmatizations and taggings.

it does lend itself to an efficient Monte Carlo approximation.


On both the tasks of morphological tagging and lemmatization, neural models have supplanted linear models in terms of performance in the high-resource case .

we are interested in producing an accurate approximation to the posterior in the presence of minimal annotated examples and potentially noisy samples produced during the sleep phase, where linear models still outperform non-linear approaches (Cotterell and Heigold, 2017).


Closest to our work is Zhou and Neubig (2017), who describe an unstructured variational autoencoder.

the exact use case of our respective models is distinct.


These settings are not optimal for all languages.

since hyperparameter exploration is computationally demanding due to the number of languages we optimized these hyperparameters on initial development data experiments over a few languages.


(2017) tends to significantly outperform the winners of the CONLL 2017 Shared Task.

in general, our models still obtain better results, outperforming Dozat et al.


The output space of this model consists of tag sets such as {POS: Adj, Gender: Masc, Number: Sing}, which are predicted for a token at each time step.

this model relies heavily on the fact that the entire space of tag sets for the LRL must match those of the HRL, which is often not the case, either due to linguistic divergence or small differences in the annotation schemes between the two languages.


We have used this work as a baseline model for comparing with our proposed method.

to earlier work on morphological tagging, we use a hybrid of neural and graphical model approaches.


(2011), they had been considered of theoretical interest only due to their incompatibility with rich feature models: incorporation of complex features resulted in jumps in asymptotic runtime complexity to impractical levels.

the recent popularization of bidirectional long-short term memory networks (bi-LSTMs; Hochreiter and Schmidhuber, 1997) to derive feature representations for parsing, given their capacity to capture long-range information, has demonstrated that one may not need to use complex feature models to obtain good accuracy (Kiperwasser and Goldberg, 2016;Cross and Huang, 2016).


As in the projective case, a mildly non-projective decoder has been known for several years (Cohen et al., 2011), corresponding to a variant of the transition-based parser of Attardi (2006).

its Opn 7 q runtimeor the Opn 6 q of a recently introduced improvedcoverage variant (Shi et al., 2018) -is still prohibitively costly in practice.


2011, all valid items of the form ri, i`1s are considered to be axioms.


2011show how the items of a variant of MH 3 can be given a transition-based interpretation under the "push computation" framework, yielding the arc-hybrid projective transition system.

such a derivation has not been made for the non-projective case (k ą 3), and the known techniques used to derive previous associations between tabular and transition-based parsers do not seem to be applicable in this case.


This makes it possible to group derivation subtrees, and the transition sequences that they yield, into "push computations" that increase the length of the stack by a constant amount.


in that paper they show that it can be tabularized in Opn 6 q using the push computation framework.


Here, we have derived it as an interpretation of the Opn 4 q MH 4 parser.

in this case the dynamic programming algorithm does not cover the full search space of the transition system: while each item in the MH 4 parser can be mapped into a computation of this MH 4 transition-based parser, the opposite is not true.


Exact inference for dependency parsing can be achieved in cubic time if the model is restricted to projective trees (Eisner, 1996).

nonprojectivity is needed for natural language parsers to satisfactorily deal with linguistic phenomena like topicalization, scrambling and extraposition, which cause crossing dependencies.


RNNs have largely replaced approaches such as the fixed-window-size feed-forward networks of Durrett and Klein (2015) in part due to their ability to capture global context.

rNNs are not the only architecture capable of summarizing large global contexts: recent work by Vaswani et al.


The perhead weight matrices now multiply a matrix P containing the same position embeddings that are used at the input to the encoder, rather than the layer input X (as in Section 2.2.1).

value vectors remain unchanged and continue to carry content-related information.


We find this simple scheme remarkably effective: it is able to outperform pretagging and can operate even in the absence of word embeddings.

its performance is ultimately not quite as good as using a character LSTM.


Larger speedups are obtained for larger transducers because the GPU can utilize the streaming multiprocessors more fully.

the overhead created by CUDA calls, device synchronization, and memory transfers between the host CPU and the device might be too expensive when the inputs are too small.


On the one hand, the guiding-feature method projects the knowledge of the source-side treebank into the target-side treebank, and utilizes extra pattern-based features as guidance for the target-side parsing, mainly for the traditional discrete-feature based parsing .

the multi-task learning method simultaneously trains two parsers on two treebanks and uses shared neural network parameters for representing common-ground syntactic knowledge (Guo et al., 2016).


The UD (universal dependencies) project 3 releases a more detailed language-generic guideline to facilitate cross-linguistically consistent annotation, containing 37 relation labels.

after in-depth study, we find that the UD guideline is very useful and comprehensive, but may not be completely compact for realistic annotation of Chinese-specific syntax.


As a strong baseline for the conversion task, the multi-task trained target-side parser ("multi-task") does not use d src during both training and evaluation.

the conversion approaches use both the sentence x and d src as inputs.


Since beam search explores analyses in descending order of probability, DISTANCE and SUR-PRISAL ought to be yoked, and indeed they are correlated at r = 0.33 or greater across all of the beam sizes k that we considered in this study.

they are reliably associated with different EEG effects.


Both models use the same information regarding the true question and answer and are trained using the same number of model parameters.

17 the EVPI model, unlike the neural baseline, additionally makes use of alternate question and answer candidates to compute its loss function.


We find that the neural models beat the non-neural baselines.

the differences between all the neural models are statistically insignificant.


Setup We use the same pretrained deep RNNs and feed-forward prediction network paradigm.

we change the input from the previous experiments, as this task is not at the word-level, but rather concerns the relationship between two words; therefore, given a word pair w c , w p for which we have a dependency arc label, we input [w c ; w p ; w c • w p ] into the classifier.


In scenarios such as media-monitoring, where the main objective is to have a robust monitoring system for specific programs, we plot the WER across the 24 programs in the test set, and we can see in figure 2 that both the glass-box and black-box estimation are following the gold-standard WER per program.

unlike predicting word countN or error count ERR, we can see that the black-box, in general, over-estimates the WER, while the glass-box system under-estimates WER similar to figure 1.


Using gender as an example, a discriminator will attempt to predict the gender, b, of each instance from h, such that training involves joint learning of both the model parameters, θ M , and the discriminator parameters θ D .

the aim of learning for these components are in opposition -we seek a h which leads to a good predictor of the target y, while being a poor representation for prediction of gender.


We need b forward and backward passes to compute derivatives at each step of the path, leading to O(br) queries.

a naive loss-based approach requires computing the exact loss for every possible change at every stage of the beam search, leading to O(brL|V |) queries.


Since this data set is part of a clinical trial, an exact text message cannot be provided as an example.

the following messages illustrate typical messages in this data set, "I've been clean for about 7 months but even now I still feel like maybe I won't make it."


Note that on the imbalanced A-CHESS data set, on the standard classification task, KCCA embeddings perform better than the other baselines across all three performance metrics.

from Table 2, GlvCC embeddings achieve a higher average Fscore and AUC over KCCA embeddings that obtain the highest precision.


However, it performs similar to the random baseline in the other corpora.

the classifier strategy performs well on both ATIS and Social Network but poorly on NLMap.


Deep learning via triplet networks was first introduced in (Hoffer and Ailon, 2015), and has since become a popular technique in metric learning (Zieba and Wang, 2017;Yao et al., 2016;Zhuang et al., 2016).

previous usages of triplet networks were based on supervised data and were applied mainly to computer vision applications such as face verification.


Note that an additional interesting comparison is to a skip-thought version obtained by learning a linear transformation of the original vectors using the triplet datasets.

no off-the-shelf algorithm is available for learning such transformation, and we leave this experiment for future work.


FrameNet data enabled the development of wide-coverage frame parsers using supervised learning (Gildea and Jurafsky, 2002;Erk and Padó, 2006;Das et al., 2014, inter alia), as well as its application to a wide range of tasks, ranging from answer extraction in Question Answering (Shen and Lapata, 2007) and Textual Entailment (Burchardt et al., 2009;Ben Aharon et al., 2010).

frame-semantic resources are arguably expensive and time-consuming to build due to difficulties in defining the frames, their granularity and domain, as well as the complexity of the construction and annotation tasks requiring expertise in the underlying knowledge.


In this paper, we focus on the simplest setup with subject-verbobject (SVO) triples and two roles, but our evaluation framework can be extended to more roles.

to the recent approaches like the one by Jauhar and Hovy (2017), our approach induces semantic frames without any supervision, yet capturing only two core roles: the subject and the object of a frame triggered by verbal predicates.


As noted in Poesio et al., 1997), bridging descriptions consider many different types of relationships between a definite description (definite generic NP) and its antecedent; e.g., synonymy, hyponymy, meronymy, events, compound nouns, etc.

in this paper we focus on identity type of relationships only.


These models rely on paths between existing relations in order to infer new associations between entities in KGs.

for relation extraction from a sentence, related pairs are not predefined and consequently all entity pairs need to be considered to extract relations.


In the combined method of HITS and K-means algorithms, we first rank the instances and patterns based on their bipartite graph and then run K-means to cluster instances in our annotated dataset.

instead of choosing the instance nearest to the centroid, we retain the one that has the highest HITS hubness score in each cluster.


In the previous NBT model, system designers have to tune the belief state update mechanism manually whenever the model is deployed to new dialogue domains.

the proposed model learns to update the belief state automatically, relying on no domain-specific validation sets to optimise DST performance.


The nested term Chicago is annotated as location in the inner span annotation.

there are only few inner span annotations.


For example, the typo "sterreichischen Außenministerlum" (should be "Außenministerium", Austrian foreign ministry) is manually annotated in the data but not detected by any of the models.


in Chinese "shī-xiān" (god of poetry) refers to the poet Li-bai and "shī-shèng" (saint of poetry) refers to the poet Du-fu.

few attempts have been made in Chinese analogical reasoning.


The metaphor annotation is at the relational level, which involves identification of metaphorical relations between source and target domain vocabulary.

scholars have different opinions on the distinction between literal and metaphorical senses.


The datasets used in these works are typically not directly applicable in the context of article commenting, and are small in scale that is unable to support the unique complexity of the new task.

our dataset consists of around 200K news articles and 4.5M human comments along with rich meta data for article categories and user votes of comments.


Despite their simplicity, they show rather good results.

while measuring normplagdet score (Table 2), M1 and M2 exhibit poor results, while tested state-of-the-art systems evenly achieve better recall and normplagdet scores.


The text highlighted indicates repetition, "#" refers to masked number.

recent studies show that there are salient problems in the attention mechanism.


(2018) found that news stories that were factchecked and found to be false spread faster and to more people than news items found to be true.

our methodology considers immediate reactions to news sources of varying credibility, so we can determine whether certain reactions or reactions to trusted or deceptive news sources evoke more or faster responses from social media users.


Our analysis of user reactions to trusted and deceptive sources on Twitter and Reddit shows significant differences in the distribution of reaction types for trusted versus deceptive news.

due to differences in the user interface, algorithmic design, or user-base, we find that Twitter users react to trusted and deceptive sources very differently than Reddit users.


A natural approach to perform text style transfer is to use a regular encoder-decoder network.

the training of such network would require parallel data.


(2017)'s model changes many words in the original sentence, significantly modifying the content.

our model produces worse results in terms of perplexity values.


We observe that a lot of the topics typically associated with older social media users in the 2011 model, 5 such as swearing, tiredness and sleep, changed their age bias.

topics popular among younger social media users -for instance, topics mentioning employable skills and meetings, percolated upward as the early adopters of social media grew older.


This suggests that in every subsequent year, the language of lateteens and early-twenties is more different from the language of their contemporaries from the year before.

compare the errors for 37-year-olds in 2011 (born in 1974) against those for 37-year-olds in 2012 (born in 1975).


Moreover, DQN agent outperforms SVM-ex by collecting additional implicit symptoms via conversing with patients.

there is still a gap between the performance of DQN agent and SVM-ex&im in terms of accuracy, which indicates that there is still rooms for the improvement of the dialogue system.


In our exploration of architectures for the sentence encoding model, we also tried using a selfattention layer following the intuition that not all words are equally important for the meaning of a sentence.

we later realized that the cross lingual sentence similarity objective is at odds with what we want the attention layer to learn.


Non-linear maps beyond those generated by feedforward neural networks have also been explored for this task (Lu et al., 2015;Shi et al., 2015;Wijaya et al., 2017;Shi et al., 2015).

no attempt was made to characterize the resulting maps.


Unsupervised or limited supervision methods for learning word translation maps have recently been proposed (Conneau et al., 2018;Zhang et al., 2017;Artetxe et al., 2017;Smith et al., 2017).

the underlying methods for learning the mapping function are similar to prior work (Xing et al., 2015).


Bouamor and Sajjad (2018) use averaged multilingual word embeddings to calculate a joint embedding of all sen-tences.

distances between all sentences are only used to extract a set of potential mutual translation.